Knowledge that Transforms

W AGE-PRICE guideposts were part of the government's economic policy from 1962 until the close of the Johnson Administration in 1968.' A particular method of policing the guideposts evolved during this period. The Administration sought, through public and private confrontations, to influence the pricing decisions of firms. We shall describe briefly this policy, propose several alternative hypotheses to explain the resultant behavior of firms, and analyze statistically those variables predicted to be associated with government success and failure in influencing firm behavior.

gives opportunity-cost/marginal-product meanings to the factor prices. Unfortunately, this is not good enough. If one wants to compute social costs of a project in terms of the economy's long-run equilibrium, then the project's physical factor requirements should also be computed in this framework, allowing for the factor-substitution possibilities which exist in the long run and which are instrumental in assuring positive marginal products for all the primary resources. To conclude, while we agree with HK that in the presence of unemployed resources money expenditures on a project overestimate social costs, we do not believe (for the reasons given in this note) that the HK model allows computation of the correct adjustments.10

The use of iterative algorithms, based on the Gauss-Seidel method or a similar approach, to solve systems of nonlinear simultaneous equations may lead to problematical situations which in theory are not surprising, but in practice are unexpected by the user. In particular, such situations may arise in the solution of econometric models for simulation purposes. One source of the problem lies in the failure of these algorithms, which repeatedly solve single equations according to some sequential ordering,1 to deal with the interaction properties of specific higher-order subsystems of closely related equations. One illustration of such a subsystem is the set of equations which determines unemployment and labor force in the Wharton Econometric Forecasting Model [1].

N the past two decades our understanding of the investment process has expanded greatly. Central to this expansion is the notion that the desired or stock of capital depends, not only upon the level of output, but also upon such factors as prices, interest rates, technological change, and tax policy. Equally central is the closely related proposition that the actual stock of capital adjusts to its optimal value only after a considerable lag, possibly several years. In contrast, dynamic input-output forecasting models typically treat investment in much the same fashion as early accelerator theories: for each industry net investment is given by the change in output multiplied by a fixed capital coefficient. Thus lags and endogenously determined changes in the capital coefficients cannot be accounted for without considerable difficulty. However valuable this traditional treatment may have been in the past (and there is no doubt that it was), a wholly different approach is now required if input-output models are to benefit fully from accumulated knowledge about the investment process. In particular, fitted investment equations, rather than matrices of capital coefficients, should be the basis for forecasts. A primary purpose of this paper is to estimate such a set of equations for 68 equipment purchasing sectors as defined in the 1958 inputoutput study [10]. The regression model, first developed by the author in [19] and subsequently used by Robert Coen [3], is a generalization of work done by Robert Hall and Dale Jorgenson [11]. Their model, in addition to handling the conventional determinants of investment, is able to account for changes in a wide variety of tax policy variables: depreciation methods, tax lives, emergency amortization provisions, and investment tax credits. These variables affect investment by altering the user cost of capital and, consequently, the optimal stock of capital. Unfortunately, the extent to which changes in the user cost affect the optimal stock of capital is not estimated by Hall and Jorgenson. Rather, the effect is constrained by assuming a Cobb-Douglas production function. To remedy this deficiency, the generalized approach presented here actually estimates the impact of the user cost on optimal stocks with the aid of a constant elasticity of substitution (CES) production function [1]. Since the Cobb-Douglas is a special case of the CES, estimates of the generalized model provide a test of the Hall-Jorgenson hypothesis. The plan of the paper is as follows. Sections II and III outline the theory of investment underlying the regression model and explain the various assumptions and sources relied upon in the construction of the data. Then, section IV presents the regression results and summarizes the most important findings. Finally, in order to illustrate the usefulness of the fitted equations, section V estimates the impact on investment of a hypothetical repeal of the tax credit for 1962-1967.

Less extreme cases, however, are even more worrisome, primarily because the distortions in the solutions are less easy to identify. As shown above, (Nim)1 and (N1m) 2 without distortions differ in this illustration by 20 per cent of their mean value. WYhile it is easy to flag this difference for a steady series like labor force, there are many series, e.g., inventory investment, for which a 20 per cent jump is nothing to cause surprise. Further, if the relevant